Indonesian Online News Research Articles

Sex, Crime and Entertainment

ABSTRACT Images linking ‘LGBT’ to sexually motivated crime are a common feature of reporting in the Indonesian online news media. In particular, ‘gay sex parties’ are a spectacle shaped by the images produced by journalists in collaboration with the police. This combination of crime and entertainment plays a crucial role in managing both in what ways LGBT is seen and by extension what is defined as belonging to the public sphere. Media images of raids take the form of videos and photographs, press conferences and reconstructions filmed at the crime scene. Although same-sex sexual acts between consenting adults are not a crime in Indonesia, the 2008 Pornography Law and various regional regulations do provide scope for detaining people on public decency grounds. However, the connection between sex and crime as a form of entertainment in which the police are central protagonists is shaped by histories of visual power that can be traced to the authoritarian New Order. By contextualising and interpreting media images of a police raid on a ‘gay sex party’ in September 2020, I describe how the genre conventions and affective force of images of LGBT are central to the state’s governance of the public sphere. Considering the relationship between sex, crime and entertainment helps to address the central role played by both unruly images and media power in regulating the boundaries of political participation and claims for justice.

Health Versus Economy: Lockdown Controversy Coverage in Indonesian Online News Platforms

Coronavirus Disease 2019 (COVID-19) pandemic has urged several countries to take an extreme measurement by imposing a lockdown policy to control the spread of the virus. Generally approved, the action might have several negative effects, primarily on economic aspects. Utilizing qualitative framing analysis, the study investigates how three national online economic and business media platforms, namely Kontan.co.id, Bisnis.com, and CNBCIndonesia.com, apply news frames to cater the lockdown-related issues to the public. The study identified four news frames: conflict, attribution of responsibility, economic consequences, and human interest. It appears that the ‘conflict frame’ is heavily used to portray lockdown policy as an ‘arena of war’ between health and economic interest. 
 
 Keywords: COVID-19 Impacts, Economic and Business Media, Lockdown, Qualitative Framing Analysis

Indonesian Online News Topics Classification using Word2Vec and K-Nearest Neighbor

News is information disseminated by newspapers, radio, television, the internet, and other media. According to the survey results, there are many news titles from various topics spread on the internet. This of course makes newsreaders have difficulty when they want to find the desired news topic to read. These problems can be solved by grouping or so-called classification. The classification process is carried out of course by using a computerized process. This study aims to classify several news topics in Indonesian language using the KNN classification model and word2vec to convert words into vectors which aim to facilitate the classification process. The use of KNN in this study also determines the optimal K value to be used. In addition to using the classification model, this study also uses a word embedding-based model, namely word2vec. The results obtained using the word2vec and KNN models have an accuracy of 89.2% with a value of K=7. The word2vec and KNN models are also superior to the support vector machine, logistic regression, and random forest classification models.

Indonesian Online News Extraction and Clustering Using Evolving Clustering

43,000 online media outlets in Indonesia publish at least one to two stories every hour. The amount of information exceeds human processing capacity, resulting in several impacts for humans, such as confusion and psychological pressure. This study proposes the Evolving Clustering method that continually adapts existing model knowledge in the real, ever-evolving environment without re-clustering the data. This study also proposes feature extraction with vector space-based stemming features to improve Indonesian language stemming. The application of the system consists of seven stages, (1) Data Acquisition, (2) Data Pipeline, (3) Keyword Feature Extraction, (4) Data Aggregation, (5) Predefined Cluster using Automatic Clustering algorithm, (6) Evolving Clustering, and (7) News Clustering Result. The experimental results show that Automatic Clustering generated 388 clusters as predefined clusters from 3.000 news. One of them is the unknown cluster. Evolving clustering runs for two days to cluster the news by streaming, resulting in a total of 611 clusters. Evolving clustering goes well, both updating models and adding models. The performance of the Evolving Clustering algorithm is quite good, as evidenced by the cluster accuracy value of 88%. However, some clusters are not right. It should be re-evaluated in the keyword feature extraction process to extract the appropriate features for grouping. In the future, this method can be developed further by adding other functions, updating and adding to the model, and evaluating.

Detection of fintech P2P lending issues in Indonesia

Financial technology (fintech) is a growing industry in Indonesia, supported by advances in the technological infrastructure. At the end of 2019, the Financial Services Authority (OJK), the financial authority in Indonesia, recorded 164 registered and licensed fintech (P2P lending) companies. However, since early 2018, the Investment Alert Task Force (SWI) and the Ministry of Communication and Information Technology have blocked 1,350 illegal fintech platforms. Illegal fintech lending practices have mechanisms beyond the responsibility and authority of the OJK, including the risk of collection and distribution of personal data. The essence of this study is to discuss the landscape of fintech P2P lending in Indonesia from Indonesian Online News data, explore cases of fintech p2p lending in Indonesia, and understand the rules and policies. Qualitative research with a case study approach and Focus Group Discussion techniques were used to obtain data from 4 stakeholders in the Fintech P2P Lending Industry in Indonesia. VOS Viewer software is used to build keywords from Indonesian Online News collections, NVIVO 12 qualitative software is used to assist data analysis. The research found the keyword clusters most frequently discussed in the Indonesian Online News collection and five case themes such as public awareness about P2P lending (user understanding), data leakage, and restriction of data access, including personal data protection, personal data fraud, illegal fintech lending, and Product marketing ethics.

The Images of Donald Trump on Online News Media in Indonesia Through Photojournalism: Media Construction and Readers’ Perception

This study aims to find out the representation of the President of the United States of America, Donald Trump, through photojournalism which is used to support the news coverage of the president in the Indonesian online news media. It is reader-response research examining photography representation in the Indonesian online news media: okezone.com, tribunnews.com, and detik.com in 2017-2020 to view the media construction and readers’ perception. The respondents consisted of the students of English Department, Universitas Sebelas Maret, Indonesia were chosen to represent the digital native online news readers in Indonesia. In order to get the readers’ perception, they were asked to “read” 15 photos of President Trump, 12 of them were taken from news coverages of three online news media: okezonecom, tribunnews.com, and detik.com; whereas the other three photos were taken from the White House official website. The finding shows that Donald Trump tends to be portrayed differently with those used by the White House official website. Furthermore, readers are able to recognize the official presidential photos among the photojournalism. Despite their awareness of the photo selections by the media, each photojournalism used in the survey is perceived both negatively and positively by the readers. Keywords: Photojournalism, online media, media construction, reader-response, Donald Trump.

Strategies of ideological polarisation in the online news media: A social actor analysis of Megawati Soekarnoputri

This article examines strategies of ideological polarisation in the discourse of the Indonesian online news media site, Kompas.com. Applying Van Leeuwen’s model of social actor analysis and van Dijk’s concept of the ideological square, the study focuses on the representation of Megawati Soekarnoputri, leader of the Indonesian Democratic Party of Struggle (PDI-P) as an icon of ideological contestation during the 2014 presidential election. Situated in the era of digital platform convergence, the analysis uncovers a pattern of strategically ambiguous representations of Megawati and her apparently transgressive actions and interactions. This practice entices readers to ‘read between the lines’ and activate their ideological repertoire to determine in-group and out-group members. It also enables Kompas.com to pursue commercial objectives and navigate journalistic constraints by obscuring explicitly ideological content. The implications are discussed in terms of the impact of online news media discourse upon democratic political engagement, and women’s political participation.

CLICK-ID: A novel dataset for Indonesian clickbait headlines.

News analysis is a popular task in Natural Language Processing (NLP). In particular, the problem of clickbait in news analysis has gained attention in recent years [1, 2]. However, the majority of the tasks has been focused on English news, in which there is already a rich representative resource. For other languages, such as Indonesian, there is still a lack of resource for clickbait tasks. Therefore, we introduce the CLICK-ID dataset of Indonesian news headlines extracted from 12 Indonesian online news publishers. It is comprised of 15,000 annotated headlines with clickbait and non-clickbait labels. Using the CLICK-ID dataset, we then developed an Indonesian clickbait classification model achieving favourable performance. We believe that this corpus will be useful for replicable experiments in clickbait detection or other experiments in NLP areas.

Support Vector Machine-Based Hoax Detection on Indonesian Online News

Support Vector Machine-Based Hoax Detection on Indonesian Online News

Data Pipeline Architecture with Near Real-Time Streaming Multiple Source Indonesian Online News Data Lake

The rapid development of information has made online news increasingly needed. Online news attracts readers' attention by providing convenience and speed in presenting news from various fields. However, the large amount (volume) of online news that spreads in a short time (velocity) and the public's need to consume news in various references (variety) can affect people's lives. Therefore, the government as the regulator and news agencies need to monitor online news circulating. Based on these problems, the researcher proposes a data lake architectural design that is suitable for online news and can run in real-time. Data lakes can solve the main problems of Big Data (volume, velocity, variety). In proposing this data lake architecture, the researcher conducted a literature study and analyzed the flow of the data lake architecture according to online news. Furthermore, the researcher will use this architecture to combine and uniform the online news data structure from several online news channels and then stream it in real-time to fill the data lake. The results of using the data lake architecture for online news will be stored on MongoDB which functions as a database to store all data for both the short and long term. Finally, this data lake will be a means to accommodate, dive into, and analyze the circulating online news data. Keywords – Data Lake, Online News, Real-Time.

WRITTEN SPEECH ACTS FOUND IN ADVERTISEMENTS ON INDONESIAN ONLINE NEWS WEBSITES

This study attempts to help senior high school English teachers in developing their teaching material about advertisement, which is included in the state mandated curriculum. Having analyzed 20 advertisements, we found two types of speech acts, namely representatives and directives. The representative acts used were 59% and directive acts were 41% of the data. The advertisement strategy in Indonesia utilized representative acts, which intended to highlight the good quality of the products or services, whereas, the use of directive acts was situational since it is usually preceded by the appearance of representative acts. Since both speech acts frequently appeared in the advertisements on Indonesian online news websites, we recommend that teachers should provide exposures toward the linguistic features, which support students’ ability to produce advertisement texts containing representative and directive acts.

Indonesian Women in Politics: Critical Analysis of Portrayal in Online News Sites

In 2004 Election, Indonesian government launched a policy mandatory 30 percent female representatives in the legislature. This is an effort to accelerate an increase of women’s representativeness in the parliament. This research describes the imaging of women as politicians in Indonesian online news sites: detikcom, Kompas.com, and Tribunnews.com. Van Dijk’s critical discourse analysis was utilized as method of this research with feminist critical analysis as the corresponding method. The result shows that the imaging of female politicians is in the domestic area. They are pictured as emotional and weak persons.

Indonesian Online News: Where Does the Bias Go?

The shift from printed articles to online articles makes online news even more important for us. This saying is also applied in Indonesia, as people in Indonesia have started to turn toward online news rather than printed ones. Due to the interest Indonesians have gathered toward online news, it is important for online news to be as objective as possible, as it becomes a primary source of information and the latest updates. For this reason, this research delves into the world of Indonesian online news to see how objective it is. This research uses Kompas.com as its subject of research, and propaganda model becomes the primary theory with critical discourse analysis (CDA) as its framework. Propaganda model uses ownership, advertising, sourcing, flak, and fear as aspects used to measure the mass media, in this case Indonesian online news. This research also uses ideology where power comes into play. Through propaganda model, ideology, and CDA, this research finds out that despite of the objectivity that is supposed to exist in Indonesian online news, in this research is Kompas.com, only subjectivity and bias can be seen from it. The bias and subjectivity stem from various aspects. Through propaganda model, this research finds out that those aspects are ownership, sponsorship, personal beliefs, and profit. Despite the fact that Kompas.com is supposed to be neutral, there is little neutrality in their articles, as their articles are generated based on the bias made by the aspects mentioned. Although this research only uses Kompas.com, Kompas.com becomes an example of how little objectivity Indonesian online news has. Being the source of information, it is expected to be objective and neutral. However, instead of being neutral, Indonesian online news is biased with certain factors affecting the way the news is written. Instead of having accurate and objective online news, it is written with bias and in non-neutral way.

INDONESIAN ONLINE NEWS: WHERE DOES THE BIAS GO?

The shift from printed articles to online articles makes online news even more important for us. This saying is also applied in Indonesia, as people in Indonesia have started to turn toward online news rather than printed ones. Due to the interest Indonesians have gathered toward online news, it is important for online news to be as objective as possible, as it becomes a primary source of information and the latest updates. For this reason, this research delves into the world of Indonesian online news to see how objective it is. This research uses Kompas.com as its subject of research, and propaganda model becomes the primary theory with critical discourse analysis (CDA) as its framework. Propaganda model uses ownership, advertising, sourcing, flak, and fear as aspects used to measure the mass media, in this case Indonesian online news. This research also uses ideology where power comes into play. Through propaganda model, ideology, and CDA, this research finds out that despite of the objectivity that is supposed to exist in Indonesian online news, in this research is Kompas.com, only subjectivity and bias can be seen from it. The bias and subjectivity stem from various aspects. Through propaganda model, this research finds out that those aspects are ownership, sponsorship, personal beliefs, and profit. Despite the fact that Kompas.com is supposed to be neutral, there is little neutrality in their articles, as their articles are generated based on the bias made by the aspects mentioned. Although this research only uses Kompas.com, Kompas.com becomes an example of how little objectivity Indonesian online news has. Being the source of information, it is expected to be objective and neutral. However, instead of being neutral, Indonesian online news is biased with certain factors affecting the way the news is written. Instead of having accurate and objective online news, it is written with bias and in non-neutral way.

Media and environment: how indonesia online news portal frames biodegradable issue?

Environmental problems are still an important issue and cannot be resolved completely until now, even after the 1970s the United Nations established a world environmental day. Plastic waste is considered the most responsible for the problems of environmental damage today. Therefore, an innovation that comes from biodegradable material, with its biodegradable product is considered to be the answer to this environmental problem. This study examines how reporting on the environment is framed in online mass media, especially in relation to biodegradable material and biodegradable products. This is qualitative research, with the object of two Indonesian online news sites, namely www.detik.com and www.okezone.com. Data collection techniques used is document studies and data analysis techniques employed is Robert N. Entman's Framing analysis model which involves four elements, namely: define problems, diagnose causes, make moral judgment, and treatment recommendation. The findings of this study are that environmental issues have not dominated online media coverage, and the biodegradable issue has not been framed thoroughly by online media.

Eksperimen Naïve Bayes Pada Deteksi Berita Hoax Berbahasa Indonesia

Website and blog are popular as a media to spread news. The validity of an article of news’s can either be valid or fake. A fake article of news is usually called a hoax news article. The purpose of making hoax news is to persuade, manipulate, affect to people to do something that contradicts or prevents the right action. A hoax news usually used threats or misleading information to make them believe things that are not real. This research proposes an experiment using naïve Bayes to detect hoax news in Bahasa Indonesia. In this research, we use our own dataset consisting of a total of 600 valid and hoax articles. We asked three reviewers to conduct manual classification for our dataset. Final tagging was obtained by adopting the maximum score from the three reviewers. In our experiment, we show that naïve Bayes can classify Indonesian online news articles with term frequency feature using the PHP-ML library component’s. We obtained an accuracy is 82.6% with static testing and 68.33% with dynamic testing. We give free access to the dataset so the future research can replicate, comparing the result and make a baseline testing.Keywords : Hoax News Detection, Naïve Bayes Classifier.

Indonesian News Classification Using Naïve Bayes and Two-Phase Feature Selection Model

Since the rise of WWW, information available online is growing rapidly. One of the example is Indonesian online news. Therefore, automatic text classification became very important task for information filtering. One of the major issue in text classification is its high dimensionality of feature space. Most of the features are irrelevant, noisy, and redundant, which may decline the accuracy of the system. Hence, feature selection is needed. Maximal Marginal Relevance for Feature Selection (MMR-FS) has been proven to be a good feature selection for text with many redundant features, but it has high computational complexity. In this paper, we propose a two-phased feature selection method. In the first phase, to lower the complexity of MMR-FS we utilize Information Gain first to reduce features. This reduced feature will be selected using MMR-FS in the second phase. The experiment result showed that our new method can reach the best accuracy by 86%. This new method could lower the complexity of MMR-FS but still retain its accuracy.

Indonesian News Classification Using Naïve Bayes and Two-Phase Feature Selection Model

Since the rise of WWW, information available online is growing rapidly. One of the example is Indonesian online news. Therefore, automatic text classification became very important task for information filtering. One of the major issue in text classification is its high dimensionality of feature space. Most of the features are irrelevant, noisy, and redundant, which may decline the accuracy of the system. Hence, feature selection is needed. Maximal Marginal Relevance for Feature Selection (MMR-FS) has been proven to be a good feature selection for text with many redundant features, but it has high computational complexity. In this paper, we propose a two-phased feature selection method. In the first phase, to lower the complexity of MMR-FS we utilize Information Gain first to reduce features. This reduced feature will be selected using MMR-FS in the second phase. The experiment result showed that our new method can reach the best accuracy by 86%. This new method could lower the complexity of MMR-FS but still retain its accuracy.

Textual Analysis of the Jakarta Post Online Representation of Presidential Candidates in Indonesia

General Election 2014 in Indonesia principally was a gate to more democratic Indonesia. People were given rights to directly vote their preferred legislative representatives and president and vice president for the next five-year period. New political learning process was introduced as all presidential candidates must show their capabilities in all aspects to attract constituents. The role of mass media became important as each candidate worked hard to gain sympathy from people. This paper examines how Indonesian online news media critically reported the presidential candidates. Further, this paper analysed how the issue were represented in the online media and why it was represented in such ways.

Keyword and Event Extraction for Thematic Map Retrieval from Indonesian Online News Site

Online news sites provide great deal of information that may be extracted and mined to create Geographical Information Retrieval (GIR) system that provides thematic map based on user query. The task requires extracting generic events and its attributes from the news corpus. This event extraction requires the keywords and topics that can help increase the accuracy of the extraction and retrieval process. We prepare a large online news corpus and compared the Latent Dirichlet Allocation (LDA) and CBOW and skip-gram model to help providing base thematic keywords which can assist the extraction process on the intended GIR system. LDA is better in terms of semantic relatedness and the CBOW and skip-gram is useful for providing semantic similarity.